Resilience will obviously remain vital amid the latest swell of challenges. The ‘are we or aren’t we?’ questions around economic recession continue, alongside downsized spend estimates that could see teams struggling to drive greater results with even fewer resources. More than just continued hardiness, however, what marketers need are smarter ways of managing change and, ideally, getting ahead of it. 

To be specific, they must work on building better systems for identifying early warning signs of sliding effectiveness, in addition to emerging opportunities, instead of simply scrambling to respond after the fact. And an essential element of doing so is embracing valuable yet often overlooked elements of proactive analytics, especially anomaly detection.

Ghosts in the machine

Even in the current age of technology-powered marketing, anomaly detection might sound more like a sci-fi invention than an everyday tool. Not as complex as it seems, the process is about uncovering patterns in data outside the norm, using artificial intelligence (AI). 

The basics tend to involve setting machine learning algorithms to assess given datasets and 

spotlight outliers that don’t tally with what they expect to find. Trained on huge stores of data about past performance and consumer behaviour, algorithms run comparisons by harnessing knowledge of what counts as typical for particular types of audience segment, campaign, seasonal offers and more. With semi, fully or unsupervised options for analysis, marketers can leave labour-intensive evaluation to machines if they choose, or apply closer levels of control.

Great, so what does that mean?

For marketers, the ultimate outcome of smart analytics is simple. When any unusual patterns are detected, they receive instant warning signals or alerts. As assessment is often focused on monitoring pre-defined metrics, this means they get immediate insight into specific areas of inconsistent performance. For example, analysis may show a product explainer video that has enjoyed high viewing completion for months is now losing audiences mid-way, or highlight sudden spikes in click through rates (CTRs) for recently released display ads.

How does it benefit marketers?

In the days marketers only had a handful of campaigns to juggle, manually monitoring for abnormal data points was feasible, although not always precise. Vast expansion in the scope of modern communications has produced data that’s increasingly difficult to organise, let alone scrutinise for discrepancies. In fact, our research reveals most CMOs (99%) are using at least 10 data sources, compared to six or fewer just three years ago, while six in ten (67%) unsurprisingly admit to feeling overwhelmed by growing volumes of available data.

Where mistakes caused by human error were already a risk, it’s becoming much more likely that marketers drowning in data will miss crucial insights: stopping them from swiftly moving with developing changes and potentially leading to unchecked performance issues.

Outlier detection tools can go some way to help reduce overlooked discrepancies and speed up insight activation. With the ability to rapidly assess large-scale marketing data — including information about cross-channel spend, delivery, and interaction — analytical engines stand a better chance of accurately capturing all possible anomalies, enabling marketers to take swift, informed action. For instance, on top of directing budget away from ineffective efforts to limit wastage, they can identify which activities are over-performing and decide where adjustments should be made to improve both in-flight ROI and future outcomes.

It’s also worth noting that unusual events can indicate the beginning of bigger shifts. Keeping an eye on anomalies once detected will allow marketers to see whether certain patterns grow into fully-fledged trends; meaning they can observe, and respond to, the unfolding effects of external factors such as consumer living costs and market flections, as they happen.

What does adoption involve?

As with any form of analytics, there are crucial ingredients for effective use beyond the evaluation tech itself. Before adopting solutions with real-time detection abilities, users first need to ensure there is a solid foundation of unified, precise, and usable data to work from. 

Almost every marketer will be familiar with the data processing mantra of “garbage in, equals garbage out”, especially in terms of AI solutions. Whatever their assessment sophistication, intelligent analytics are only as reliable as the data feeding them. If data is flawed and fragmented, it’s highly probable that choices guided by the insights they produce will fail to hit the intended mark, or worse, result in negative impacts for brands. 

While the work needed to fine-tune data fundamentals will clearly depend on individual needs and circumstances, marketers can make a start by checking whether systems meet a basic list of criteria. Data stores need to be comprehensive collections of all relevant sources, accurate, and up to date. Analysis will also be a lot smoother, and reliable, when inflowing information is automatically integrated, cleansed, synced, and merged, ready for evaluation. 

Not only can anomaly detection assist with identifying any patterns but it can also provide critical intelligence to maximise performance, which can be replicated in future campaigns. Anomaly detection also tends to work better when companies already have a higher level of data maturity, including use of machine learning (ML) that helps them pinpoint where to focus detection analysis. For example, data mature companies know what KPIs are relevant for their business, how to track them and what KPIs are relevant for which dimension. The AI and ML algorithms can then help classify and analyse these two parameters, indicating how significant an outlier would be for this combination. For instance, a small outlier for an important KPI in the most important market should ring the alarm bells more than a huge outlier on a vanity metric.

Despite acute awareness that their remit has become more complicated, many marketers are hastily reacting to each new curve ball, instead of rolling with and turning fresh developments to their advantage. By providing immediate indicators of deviations from the norm, anomaly detection can enable marketers to see change coming and make proactive decisions about where, how, and when their approach should be adapted for better results. All of which means anomaly detection is a key agility asset for the modern analytical toolkit.

The post WTF is Anomaly Detection? appeared first on PerformanceIN.

As we face a cost-of-living crisis, and general economic fragility, consumers are having to constantly evaluate the value of products and services they use. There has been a genuine fall in income for everyone, and this is having a knock-on effect for brands – some of which are benefitting, while many others are losing their customers.

What value means to brands

We’re seeing German discount grocery chains, Aldi and Lidl, growing their market share, while services like Netflix are losing subscribers. Brands are having to prove their products or services to provide consumers with the much sought-after value they seek, and marketers are having to re-evaluate what value now represents for them.

The current economic climate is proving to be a far bigger challenge for brands than what they faced during the pandemic. As a result, value has become one of – if not the – most important aspect of their campaigns. With marketers increasingly unsure of who their target customer is, many are struggling to find the value they are desperately looking for.

Nonetheless, it’s not the time for marketers to reduce their budgets – it’s time to continue investing and show them why your brand provides value to their lives. To do this, marketers must see value as being closely tied to brand and performance, with all three working together to get the most out of their campaigns.

The changing face of value

Marketers must look beyond the transactional media investment and consider the role that improving their brand and driving performance have to play in providing value.

When looking to protect or grow market share, insight and creativity must be at the heart of the campaign. Insights help understand audience behaviour to form the strong foundations for the campaign, while creativity ensures that audiences are engaged and interested in the advertising being served to them.

Performance should be driven by utilising KPIs that matter, with a particular focus on attention metrics and how these can help brands to produce the most effective and efficient advertising. Ultimately, every campaign is different and brands must focus on the performance indicators that are more relevant to their needs. Value can’t be found in viewability continuing to be the standard metric for measuring the quality of impressions.

Getting brand and performance right will automatically create value for brands, because they’ll be able confidently identify who their target customer is, and ensure these are the people that are being reached at the right time. This will help to encourage current customers to continue buying from the brand, while proving to new customers that the brand can add value to their lives.

Understanding the link between value, brand, and performance isn’t just about generating value for the brand, but also delivering value for the consumer, both in and out of difficult economic climates. Tying brand and performance to value will ensure that both consumers and advertisers benefit, even as the definition of value evolves. 

The post WTF is Value and Why Should Marketers Care? appeared first on PerformanceIN.

How does it work?

Online images are usually uploaded directly to a site in a traditional format such as JPEG, PNG, or GIF. With image streaming, images are uploaded to a secure, password-protected central server, which generates a specific embed code that is then pasted into a webpage editor, such as WordPress or Weebly. These embedded images are then displayed on a webpage, similar to the way a normal uploaded image would. The difference is, this method incorporates a number of additional security features, powerful metadata, and interactive controls, which together provide image owners, publishers, and advertisers with a unique opportunity to enhance both security and end-user experience.

Security: Studies have shown that more than 2.5 billion images are stolen daily, fueled by the way images are published today on various social platforms and websites. While sharing culture provides unprecedented global reach and connection, it also comes with a variety of hidden costs, robbing image creators of fair compensation and increasing the risk of altered imagery spreading misinformation and disinformation online. Images are copied, downloaded, and redistributed without consent or awareness of the original source. Image streaming can improve this system: right-click, screenshot, and drag-and-drop actions are all disabled, instead displaying copyright notices that direct the user to a trackable embed code. Not only does this protection prevent unauthorised copies being made, but with each image being streamed from a single file on a secure server, creators and publishers have the ability to see and control where their content appears across the web.

Context: When uploading an image to the central server, image owners can attach uneditable metadata – captions, details of image content, and attribution – that stays with it wherever it’s displayed. An additional layer of control is provided via a list of the URLs and domains that host the image, giving the owner an opportunity to deactivate the image from view on a particular site at any time. Once an image has been published, it is also possible to gather analytics data, which gives publishers and image owners unprecedented insights into how the content is viewed, shared, and enjoyed. Performance can be measured through the number of views, clicks, and shares an image receives and it is even possible to see where shares and theft attempts take place.

Interactive controls: The end-user benefits too. While publishers and website owners often upload images in the lowest resolution possible to keep buffering times down, streamed images keep sites light even when extremely high resolutions are used as they are not permanently uploaded to the page. They also adapt to individual user display resolutions and make it possible for audiences to view pictures in full-screen mode or zoom into the finest details. This makes image viewing more interactive and boosts engagement.

Furthermore, the publisher can choose which features to embed: if a sharing option is included, this ensures the image is circulated consensually.

Why does it matter?

These features benefit the whole industry and introduce fairness, transparency, and equality into the supply chain. Here’s how:

Advertisers: Image streaming is a sure-fire way to guarantee high-quality contextual coverage and high-visibility placement. By using streaming technology, advertisers can activate in-image advertising, whereby an ad is shared within the space that an image is already displayed – often the focal point of a piece – sliding into the frame while a consumer is looking at it.

A combination of AI and metadata ensures the ads are targeted in contextually relevant environments, based on the exact origin and context of the image. For example, a picture of a football team could switch to an advertised football jersey for that team and back again in an article on the latest Premier League developments. Considering the uncertainty of third-party cookies and their future alternative, plus the ever-looming concern of ads being placed alongside unsuitable content, in-image advertising provides a crucial solution to delivering relevant content that is also brand safe, while respecting user privacy.

Publishers: Publishers, from bloggers and news outlets to image libraries, can reap the benefits of streaming images, including increased advertising revenue and the ability to offer a more engaging and effective user experience. In addition to this, since image owners are able to monetise their images through advertising, many will consider providing them to publishers for free, addressing a significant cost of more traditional licensing models.

Image/content owners: When it comes to the contemporary media landscape, it’s often image owners who come out the worst. Their work is redistributed without consent, copyright notice, or adequate compensation. Not only does image streaming allow image creators – from photographers to graphic artists – to keep track of their work with guaranteed accreditation, but it also ensures high visual quality whichever device it’s on, while empowering them to embrace a new stream of revenue through in-image advertising.

Going forwards

Image streaming is a revolutionary technology that not only supports a more transparent and equal media ecosystem, but also offers new models of monetisation. Instead of spending considerable time and resources trying to trace stolen images, streaming images can prevent their theft, which is currently all too easy to accomplish. All streamed images are protected and tracked, which helps brands, publishers, and image owners know how, where, and when audiences interact with their content. It also offers a much-needed opportunity for truly contextually relevant, brand-safe placements for advertisers. In short, image streaming creates opportunities at every stage of the media and image supply chain – a win-win situation for image owners, advertisers, and publishers.

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The initiative, which allows publishers to organise their audience composition in a standardised manner, does seem to bring back a level of control over the publisher’s business…

But how exactly do SDAs work, and are they going to emerge as one of the answers to how advertisers and publishers can evolve from the cookie-based system that currently underpins online advertising?

The post WTF Will Seller-Defined Audiences Do For Digital Advertising? appeared first on PerformanceIN.

A decentralised ad exchange, on the other hand, distributes power amongst participants in the exchange, including the people being served ads. The exchange vendor is still responsible for all the development and support a customer would expect but – as the exchange itself runs on a blockchain – these customers are also stakeholders who are rewarded for their participation.

If that sounds confusing, you’re not alone. Blockchain is an up-and-coming technology that can be difficult to understand, but you don’t need to learn cryptography to use a blockchain-based exchange, the same way you don’t have to be a machine learning expert to buy and sell ads with Google and Meta.

What matters are the benefits to the end user, so let’s explore what decentralised programmatic offers businesses buying and selling ads on the open web.

Distributed ledgers eliminate fraud

A forecasted $68 billion of digital advertising spend will be lost to fraud this year, up from $59 billion in 2021. Fake websites, click farms, and sophisticated bots have kept programmatic exchanges in a game of cat and mouse with fraudsters since its inception. Without a fundamental shift in how exchanges operate, billions of dollars in revenue will continue to be stolen every year. 

In a blockchain-based exchange, a complete ledger of transactions is distributed amongst all participations, and any changes made to this ledger are reflected across the entire network. Altering data on a blockchain is not possible as every transaction is linked to all transactions before it (the “chain” part of a blockchain), so any attempt to change a record will be immediately invalid.

This ledger can be both monitored in real-time and explored historically, right back to its first use. Fraud simply can’t exist in such an environment as it would be exposed the moment it is attempted, effectively offering built-in real-time fraud protection for all customers as standard. If all exchanges were decentralised, the annual cost of programmatic fraud wouldn’t be reduced, it would be eliminated.

Transparent transactions build trust

Decentralised ad exchanges build a common trust not only between advertisers and publishers but between users of the exchange and the vendor. Centralised exchanges obscure their inner workings, so its users are never entirely certain of the accuracy of the data they receive, where their fees end up going, or whether inventory is being fairly valued.

Lack of trust in typical exchanges is compounded by the variability of fees. Analysis by Adalytics found that while this could sometimes be attributed to inventory value (for example, 7% or 20% depending on placement), one major SSP was charging a publisher between 7% and 42% with no clear reason for how or why such a wide range in fees was possible. 

In a blockchain-based exchange, buyers and sellers can track the journey of every dollar spent, eliminating any possibility of untraceable costs, which the ISBA found accounts for 15% of advertiser spend on average. There is no middleman taking a cut in a blockchain; the entire exchange operates simply through ad buyers and ad sellers trading with total transparency.

The transactions logged in a decentralised ad exchange include more than just receipts from buying and selling: whether an ad was served, where it was served, and how many interactions it received are all recorded as well, allowing for complete and accurate campaign auditing.

Fair value exchange means minimal fees

The average cut taken by the DSP and SSP when a media buyer bids for an ad slot is 15 – 35% and can rise significantly higher, with cumulative fees for some impressions leaving the publisher with just 2% of an advertiser’s spend. As decentralised exchanges don’t derive their profits from fees, buyers and selling are only charged a negligible amount (1%–2%) for each impression to cover running costs.

Which inevitably leads to the question: how does a decentralised programmatic exchange generate revenue? The solution is “tokenomics”, where participants in the exchange purchase or lease tokens which are then used to buy and sell advertisements. The more the exchange is used, the higher the value of these tokens, and it’s here that vendors – and all stakeholders in the exchange – can make a profit.

In the long run, the opportunity for everyone who participates in a platform to benefit from its success is the most exciting feature of the Web 3.0 ecosystem, and digital advertising has long been overdue for a more fair and equitable value exchange for advertisers, publishers, and consumers.

The post WTF is… a Decentralised Ad Exchange? appeared first on PerformanceIN.

SKAdNetwork was created as an alternative to Apple’s Identifier for Advertisers (IDFA); a familiar advertising ID attached to a user’s device. IDFA allows advertisers to track user interactions to attribute them to a campaign ‘deterministically’. The initial rollout of SKAdNetwork in March 2018 had no immediate impact on the mobile marketing industry, with advertisers continuing to use IDFA as normal, blissfully unaware of what was coming. However, in June 2020, in the context of rising consumer awareness around privacy and the value of personal data, Apple shocked the mobile advertising industry by announcing major changes to how advertisers could access its IDFA. 

The most seismic change was in the form of the introduction of app tracking transparency (ATT). This would require users to opt-in to share their data with advertisers every time they opened a new app. In doing so, they would allow advertisers to continue using familiar deterministic attribution with IDFA. However, when ATT was first introduced, estimated opt-in rates were reported to be in the region of 15%. 

WTF actually is a SKAdNetwork?

Starting from the beginning, the ‘SK’ is shorthand for ‘StoreKit’, Apple’s developer framework; and the technology is an aggregate for measuring mobile ad campaigns for iOS apps. Put (relatively) simply, it’s a privacy-friendly framework designed to provide advertisers and ad networks with aggregated campaign performance data about acquired users, without revealing any user level details. Because it is privacy-centric at its core, it has a range of limitations when compared to the previously dominant IDFA and deterministic attribution. There is also an additional layer of complexity for advertisers that want to comprehend the framework’s data.

SKAdNetwork differs greatly from IDFA and deterministic attribution in that it doesn’t reveal user or device-level data to attribute impressions and clicks to instals of iOS apps. Contrary to popular belief, the IDFA hasn’t been ‘killed’ or ‘ended’, but ATT does now require users to permit advertisers to use IDFA, whereas previously it would work in the background to track users automatically. If iOS users opt-in when they open a new app, advertisers can then track them and access their device ID.

If the user refuses to share their data, the advertisers must rely on more subtle signals under the anonymised SKAdNetwork, or use frowned-upon fingerprinting methods. Fingerprinting attribution runs the risk of advertised apps being rejected from the App Store in the future. Apple calls out fingerprinting as not permissible in their guidelines, but has not clamped down on it yet. SKAdNetwork doesn’t carry such risks; it works for both opted-in and opted-out users while protecting the privacy of each. 

What has its impact been? 

With a 24 hour data privacy threshold and no real-time marketing events available, SKAdNetwork made it incredibly difficult to determine where users were coming from, what their value was, and what marketing campaigns were working. This was especially true at the onset, when SKAdNetwork data was being passed from Apple to ad networks, rather than directly to advertisers themselves – a situation that was resolved in September 2021. From iOS 15 onwards, advertisers have received copies of the ‘postback’ (data) that is sent to ad networks, but only a minority of advertisers are currently making use of this.

That’s because, even with unfettered access to SKAdNetwork data, publishers required commitment from development teams to support it, and data scientists to make sense of it. They were no longer able to lean on traditional mobile measurement partners (MMPs) for install data, standard analytics (DAUs, sessions, revenue, retention), LTV metrics, ROI and ROAS, as they had previously. 

This has driven many app developers and publishers – most (68%) of whom don’t have access to in-house data science – to more seriously explore automating their marketing processes such as the collection and storage of data. Manually collecting, importing, and analysing vast, disparate, anonymised SKAdNetwork data sets is enough to grind any marketing team to a halt. 

What are the benefits? 

Can there be benefits for the mobile marketing industry if it can’t show targeted ads to the majority of users? Potentially. Let’s wake up and smell the coffee; the majority of users, particularly in privacy-aware European markets such as Germany, aren’t going to opt-in to ATT. This makes SKAdNetwork, which functions for all users regardless of their opt-in status, the only white-hat method for advertisers to optimise performance for 100% of iOS users.

If SKAdNetwork does reach mass adoption, it could therefore represent a reset in the relationship between users and advertisers in the long-term. However, given the choice, the vast majority of advertisers still use deterministic attribution for opted-in users, delaying the short-term pain of getting to grips with SKAdNetwork. 

SKAdNetwork, with Apple as the ultimate arbiter, is changing the relationship between advertisers and self-attributing networks (SANs) like Facebook and Snapchat. These networks can no longer limit access to their data and are treated in the same way as smaller ad networks.

Additionally, ad networks have hidden their sources of traffic (app names) from advertisers, but under SKAdNetwork, this is no longer possible. One of the SKAdNetwork fields shared with advertisers is ‘source-app-id’ which corresponds with the app ID in the App Store – giving advertisers much greater clarity. 

Another major upside of SKAdNetwork is the potential to cut down on fraudulent attributions by raising the technical bar for fraudsters. Previously, bad actors could work the system with relative ease and utilise hidden adverts, fake ads or fake clicks because there was no ‘middle-man’ to verify information between user and publisher. SKAdNetwork improves security by adding a cryptographic signature to each ad impression verifying the integral data, without compromising any information on the user who viewed or clicked the ad.

Other improvements include measurement of the performance of web-to-app ads which direct to the App Store, which will now be attributed to the SKAdNetwork, a feature previously left out which meant many users fell through the cracks. With these updates, Apple potentially also gets closer to ending device fingerprinting, a practice it has always been clear it doesn’t allow, but may have tacitly permitted while it gets SKAdNetwork’s full suite of features online.

WTF happens next?

In some ways, SKAdNetwork has been something like a live service video game that was released before it was really ready. It is improving, though. As a part of the recent SKAdNetwork 4.0 update Apple announced it would lift the cap on the amount of data advertisers could pass with campaign ID, in addition to other gradual improvements which will see the framework roll out all features and reach its full potential, at scale, at some point in 2023.

One major impact of SKAdNetwork was a redistribution of ad spend away from iOS towards Android – both Google Play and alternative global app stores such as Huawei AppGallery and Samsung Galaxy Store. This is creating a more diverse market, with more choice for developers and advertisers outside of just iOS and Google. Likewise, Google has responded to Apple’s privacy-first changes with some of its own, announcing an expansion of its Privacy Sandbox to mobile and potentially signalling the end of the Google Ad ID within the next two years. 

It remains to be seen whether a more diverse, competitive market is the long-term direction of travel. Slowly but surely, Apple is refining the SKAdNetwork to better capture data for advertisers while protecting users. It is certain, though, that the long-term direction of mobile advertising is privacy-first and, for those advertisers still mourning the IDFA, it’s time to move on to SKAdNetwork. 

The post WTF is a SKAdNetwork? appeared first on PerformanceIN.

Read more from James Addlestone here…

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The landscape is shifting, with the legacy ways of tracking performance coming to an end. How, as an industry, can we continue to correctly attribute sales? A new, refined strategy is needed, and probabilistic attribution may be able to assist marketers with exactly that.

Probabilistic attribution is a mobile attribution technique which is often used to recognise a mobile, laptop or browser device.

The death of deterministic methods

Apple’s Identifier for Advertisers (IDFA), Google’s Advertising Identifier (GAID) and cookies are what’s known as deterministic methods. These methods use a unique identifier which is associated with a particular device.

Obviously, we all know that an increasing number of users are opting out of tracking across the board, meaning there is an ever-growing amount of anonymous, ‘invisible’ users on the web.

As Nick Yang said in a piece we published in February this year, we are already witnessing the impact the existing restrictions around third party cookie tracking are having on traditional Multi Touch Attribution (MTA).

“All marketing platforms use cookies to record touchpoints and use that data to attribute conversion credit accordingly. So everyone is impacted.”

Nick Yang, Head of Media at 55

How does probabilistic attribution work?

It’s in the name – probabilistic methods work by basing attribution on probability. It relies on Machine Learning (ML) to identify conversions that will probably happen. Probabilistic methods work by collecting behavioural data and trying to match it with other records that already exist.

The technique is regarded as being much less accurate, so is usually only used when deterministic methods are not available. However, marketers may need to get used to using probabilistic attribution as we head into the cookieless future.

What is it used for?

Since deterministic device identifiers are usually only available in mobile app environments, one of the main ways that probabilistic attribution is currently used is in mobile web environments. This could be, for instance, in a web-based campaign targeting mobile devices. Probabilistic attribution can also be used for tracking email campaigns that users may open on a mobile device.

The ultimate goal of probabilistic attribution is to ensure that advertisers can still determine performance and ROI for their campaigns, despite the impending doom of deterministic identifiers.

It’s a good idea to get used to using probabilistic attribution, even if it’s just a test or a tiny part of your tracking for now, to ensure you don’t get left behind.

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This is where the opportunity to optimise publisher yield with an auction framework like Header Bidding comes in. Through its ability to streamline media trading for publishers and provide transparent access to premium inventory for advertisers, the solution has become a mainstay of online programmatic. Indeed, more than half (55%) of publishers have adopted Header Bidding to sell video inventory and 22% leverage it for Connected TV (CTV).

So precisely what is Header Bidding, how does it work and how is the opportunity for yield optimisation being used in other advertising channels such as programmatic digital out of home (DOOH)? 

What is Header Bidding?

Before the emergence of Header Bidding, publishers would typically use a waterfall auction to trade their inventory. In this model, inventory is offered to one ad network at a time and then moved onto the next if the floor price isn’t met until all impressions are (hopefully) sold. Although this process works in practice, publishers risk missing out on revenue because of the lack of competition for ad impressions. 

Header Bidding is an advanced method of programmatic trading that allows publishers to offer their inventory to multiple SSPs using a unified auction framework. As a result, publishers can invite various demand sources to simultaneously bid on available inventory.

What are the key advantages for publishers?

Firstly, Header Bidding delivers an increase in yield optimisation versus the waterfall model. Studies have found that publishers can significantly grow revenue, with one example showcasing that it increased revenue by 70%. It does this by enabling more competitive auctions where inventory is made available to a larger number of ad buyers who must then outbid one another to secure impressions, leading to higher CPMs. 

In comparison, waterfall auctions accept the first bid that meets the publisher’s floor price and don’t offer visibility on ad buyers that are willing to pay more. By unifying the auction across multiple SSPs, Header Bidding provides this visibility, accepts the highest bid, and maximises ad revenue for the publisher.    

Secondly, Header Bidding allows publishers to be selective over which demand partners they include in their auctions. This gives them more control of their operations, enabling them to manage their resources more efficiently in the bidding process, and ensuring they serve quality promotions to their users. With an improved user experience comes better retention and loyalty, further heightening the value of inventory.

Finally, Header Bidding solutions are easy to manage. There are a number of experienced players in the market that can handle all elements of advertising operations, so publishers do not need to invest the time and resources in building, running, and updating their solutions in house. 

What does Header Bidding bring to advertisers?

With Header bidding, each advertiser has an equal opportunity to secure inventory if their bid wins. This method of programmatic trading gives ad buyers more transparency and greater access to premium ad placements, which can boost the reach and impact of their ad campaigns. 

How is Header Bidding changing programmatic trading across the advertising landscape?

Programmatic trading allows advertisers to boost efficiency, increase precision, and optimise their bidding strategies. In the programmatic DOOH landscape, for instance, this type of trading makes inventory more accessible because they are changed more frequently. Ad buyers can purchase space for an hour and ensure they reach target audiences at the optimal time. 

Solutions for programmatic DOOH inform bidding strategies using location data and patterns in audience behaviours. With this, advertisers can pinpoint the inventory that is best placed to capture the attention of their target audiences. Automated trading solutions based on this type of yield optimisation improve this system by giving advertisers access to a broader selection of premium inventory.

For media owners, a yield optimisation solution enables the integration and management of any number of SSPs without the need for additional development work. As a result, publishers can see an overview of programmatic DOOH activity on a single dashboard, streamlining advertising operations. Moreover, working with one unified auction enables publishers to optimise yield with competitive pricing while minimising reporting discrepancies. 

With programmatic yield optimisation use cases spanning across advertising channels, from digital video to programmatic DOOH, many publishers have begun to embrace it for the promises of increased revenue. Combined with the growing awareness and adoption of programmatic trading, it’s only a matter of time before this solution becomes widely understood and recognised across all sides of the ecosystem.

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Evolution: from Web 1.0 to Web 3.0

Web 1.0 was essentially just a “collection” of static sites with information for browsing –  you can think of it as newspapers or a giant digital library. Users could download books, read articles, watch the news and download their favourite music and movies. Only resource owners and webmasters could create and control such digital content. Users could not interact with web pages – no authorisation, logins, signups, no comment posting, no content creation or editing whatsoever was possible between 1991 to 2004.

Web 2.0 spans the timeframe from 2004 until now. The advent of social media, or Web 2.0 has turned the internet into an interactive space thanks to advances in web technologies such as Javascript, HTML5, and CSS3. Based on those technologies YouTube, Facebook, Wikipedia, and other interactive platforms were created – they gave a real start to the user-generated content era. 

Now we share personal information with a large number of sites. We create content, leave comments, register personal accounts, make purchases and leave plenty of digital traces on the web. Resource owners, meanwhile, collect this information and use it to personalise our digital experience by showing the recommended content or offering us products and services based on gathered data. Mainly it happens through performance marketing methods such as PPC advertising and social media. 

Web 3.0 is a new era of the internet that will be based on Blockchain or other decentralisation technologies. Blockchain allows anyone to deploy nodes and manage them, thus, the internet will no longer be “living” on servers. In simple terms, no web service or source will be able to block or delete your account because there will be no centralised authority, decisions will be approved by network participants, everyone on the network will have permission to use a specific service, and so on. In the nutshell, the work of Web 3.0 will be governed by the principles of technology it will be based on – blockchain. 

Web 3.0 – what’s significantly new?

The main advantage of Web 3.0 is that it is supposed to solve the biggest problem of Web 2.0: the collection of personal data by web sources, which can be misused or sold to third parties. In Web 3.0, the web is decentralised so no one controls it, and decentralised applications (Dapps) built on top of the web are open. 

The openness of the decentralised web means that no party can control the data or restrict access to the web resource or service. Theta network is a good example of it, this Web 3-based service for creating, publishing, and sharing video content stores content in nodes. In addition to this blockchain solutions have many other advantages. For example, all interaction and all data on the network can be tokenised, which means that users will get the opportunity to be rewarded for certain actions (e.g sharing their data, etc.)

How Web 3.0 will reshape the performance marketing landscape

Programmatic: The monopoly of walled gardens will be gone. Almost 90% of sites and web services are now focused in the hands of several digital giants. With this digital platforms own almost all that the user posts and creates. Services sell this information to advertisers or use this information on their own ad platforms to stimulate the economy of attention.

In Web 3.0 tokens and cryptocurrency can be used to develop completely new business models and economies. For example, ads on a decentralised web will no longer rely on selling user data to advertisers but instead will reward users with tokens for viewing ads. An example of such initiative we can see in Brave Browser and their Basic Attention Token (BAT). In such circumstances, the monopoly of walled gardens will go to the past, users will get a fair value exchange for their data, and advertisers will compete on equal terms.

Ad platforms: More security and transparency. Blockchain is supposed to decentralise and redefine the digital ecosystem inside out. Another bonus is, innate to this technology data security can effectively fight problems associated with ad fraud and privacy protection as blockchain eliminates the risks of data leakages. In the blockchain, all data is getting registered at the decentralised ledger and can’t be modified or erased by a single entity. When users will have direct control over their data and privacy, the web space will turn highly user-centric, transparent, which will promote consumer trust. 

Banner & display ads: improved targeting and user retention. No one likes to be bombarded with irrelevant ads, but when commercials match the interests of users, advertising can be useful and even helpful. The Web 2.0 era gave a giant push to individualised advertising experience based on user data. This helped to suit advertising experience up to the potential customers. However, the growing privacy concerns resulted in tightening global privacy regulations (GDPR, CCPA), which made it impossible to use user data for targeting without data collection consent. Web 3.0 aims to improve ad matching by using smarter AI and ML systems to identify and refine relevant audiences. The Semantic Web allows computers to analyse a wealth of data on the web, including content, transactions, and the rest of subtle details. 

Search Engine Marketing: going beyond the keywords. Search engines have already evolved to the point that they don’t rely only on keywords, they use dozens of other signals to correctly interpret the true meaning of the webpage (including latent semantic indexing). The application of the semantic web will allow computers to decode the meaning of the content through more profound data analysis. Along with this Google has launched Google Commerce Search 3.0 so that brands could increase sales and usability via customised results. It is enabling a direct connection between online product data presenting visitors with the certain information that they need to make purchase decisions. 

Social media: power back to content owners. In Web 2.0 content that authors create on social media technically belongs to platform owners – Facebook, Twitter, YourTube etc. In case the internet will be based on blockchain, all publications on social media will belong to the authors. A token-driven web economy means the users will be able to monetise their data and content in social media. They also will be able to buy items directly from online stores (in exchange for tokens). Smart contracts (which are part of blockchain technology) will automatically check the transactions and verify if conditions are met that will contribute to higher trust between brands and customers.

To wrap it up

Web 3.0 will embark us on the new chapter of the digital economy where decentralisation helps users receive value for their data and advertisers get equivalent chances to access it. Additionally, with a decentralised ledger of blockchain the user data will no longer be the subject of hacking, which will elevate user trust. The users will obtain ownership over their data and content, so they will be able to monetise it and share with brands in exchange for tokens. The innovations in semantics and data processing, meanwhile, will help advertisers to enrich the advertising experience without compromising privacy and user experience.

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